Evolutionary modeling and inference of gene network

Authors:
Shin Ando;Erina Sakamoto;Hitoshi Iba
Affiliations:
Department of Electronics Engineering, Graduate School of Engineering, University of Tokyo, Tokyo, Japan;Department of Information and Communication Engineering, Graduate School of Engineering, University of Tokyo, Tokyo, Japan;Department of Frontier Informatics, Graduate School of Frontier Science, University of Tokyo, Tokyo, Japan
Venue:
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
Year:
2002

Citing 8
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Abstract

This paper describes an Evolutionary Modeling (EM) approach to building causal model of differential equation system from time series data. The main target of the modeling is the gene regulatory network. A hybrid method of Genetic Programming (GP) and statistical analysis is featured in our work. GP and Least Mean Square method (LMS) were combined to identify a concise form of regulation between the variables from a given set of time series. Our approach was evaluated in several real-world problems. Further, Monte Carlo analysis is applied to indicate the robust and significant influence from the results for gene network analysis purpose.